Quantum机器学习目前正在受到极大的关注，但是与实用应用的经典机器学习技术相比，其有用性尚不清楚。但是，有迹象表明，某些量子机学习算法可能会提高其经典同行的培训能力 - 在很少有培训数据的情况下，这在情况下可能特别有益。这种情况自然出现在医学分类任务中。在本文中，提出了不同的杂种量子卷积神经网络（QCCNN），提出了不同的量子电路设计和编码技术。它们应用于二维医学成像数据，例如在计算机断层扫描中具有不同的，潜在的恶性病变。这些QCCNN的性能已经与它们的经典同行之一相似，因此鼓励进一步研究将这些算法应用于医学成像任务的方向。

Digital platforms, including online forums and helplines, have emerged as avenues of support for caregivers suffering from postpartum mental health distress. Understanding support seekers' experiences as shared on these platforms could provide crucial insight into caregivers' needs during this vulnerable time. In the current work, we provide a descriptive analysis of the concerns, psychological states, and motivations shared by healthy and distressed postpartum support seekers on two digital platforms, a one-on-one digital helpline and a publicly available online forum. Using a combination of human annotations, dictionary models and unsupervised techniques, we find stark differences between the experiences of distressed and healthy mothers. Distressed mothers described interpersonal problems and a lack of support, with 8.60% - 14.56% reporting severe symptoms including suicidal ideation. In contrast, the majority of healthy mothers described childcare issues, such as questions about breastfeeding or sleeping, and reported no severe mental health concerns. Across the two digital platforms, we found that distressed mothers shared similar content. However, the patterns of speech and affect shared by distressed mothers differed between the helpline vs. the online forum, suggesting the design of these platforms may shape meaningful measures of their support-seeking experiences. Our results provide new insight into the experiences of caregivers suffering from postpartum mental health distress. We conclude by discussing methodological considerations for understanding content shared by support seekers and design considerations for the next generation of support tools for postpartum parents.

State-of-the-art language models are often accurate on many question-answering benchmarks with well-defined questions. Yet, in real settings questions are often unanswerable without asking the user for clarifying information. We show that current SotA models often do not ask the user for clarification when presented with imprecise questions and instead provide incorrect answers or "hallucinate". To address this, we introduce CLAM, a framework that first uses the model to detect ambiguous questions, and if an ambiguous question is detected, prompts the model to ask the user for clarification. Furthermore, we show how to construct a scalable and cost-effective automatic evaluation protocol using an oracle language model with privileged information to provide clarifying information. We show that our method achieves a 20.15 percentage point accuracy improvement over SotA on a novel ambiguous question-answering answering data set derived from TriviaQA.

Compartmental models are a tool commonly used in epidemiology for the mathematical modelling of the spread of infectious diseases, with their most popular representative being the Susceptible-Infected-Removed (SIR) model and its derivatives. However, current SIR models are bounded in their capabilities to model government policies in the form of non-pharmaceutical interventions (NPIs) and weather effects and offer limited predictive power. More capable alternatives such as agent based models (ABMs) are computationally expensive and require specialized hardware. We introduce a neural network augmented SIR model that can be run on commodity hardware, takes NPIs and weather effects into account and offers improved predictive power as well as counterfactual analysis capabilities. We demonstrate our models improvement of the state-of-the-art modeling COVID-19 in Austria during the 03.2020 to 03.2021 period and provide an outlook for the future up to 01.2024.

Convolutional neural networks (CNN) define the state-of-the-art solution on many perceptual tasks. However, current CNN approaches largely remain vulnerable against adversarial perturbations of the input that have been crafted specifically to fool the system while being quasi-imperceptible to the human eye. In recent years, various approaches have been proposed to defend CNNs against such attacks, for example by model hardening or by adding explicit defence mechanisms. Thereby, a small "detector" is included in the network and trained on the binary classification task of distinguishing genuine data from data containing adversarial perturbations. In this work, we propose a simple and light-weight detector, which leverages recent findings on the relation between networks' local intrinsic dimensionality (LID) and adversarial attacks. Based on a re-interpretation of the LID measure and several simple adaptations, we surpass the state-of-the-art on adversarial detection by a significant margin and reach almost perfect results in terms of F1-score for several networks and datasets. Sources available at: https://github.com/adverML/multiLID

在本文中，我们考虑了通过风险最小化监督学习中变异模型的问题。我们的目标是通过双层优化和通过算法展开对学习变异模型的两种方法进行更深入的了解。前者将变分模型视为低于风险最小化问题的较低级别优化问题，而后者将较低级别优化问题替换为解决上述问题的算法。两种方法都在实践中使用，但是从计算的角度来看，展开要简单得多。为了分析和比较两种方法，我们考虑了一个简单的玩具模型，并明确计算所有风险和各自的估计器。我们表明，展开可能比双重优化方法更好，而且展开的性能可以显着取决于进一步的参数，有时会以意外的方式：虽然展开的算法的步骤大小很重要，但展开的迭代数量只有很重要如果数字是偶数或奇数，并且这两种情况截然不同。

我们研究Claire（一种差异性多形状，多-GPU图像注册算法和软件）的性能 - 在具有数十亿素素的大规模生物医学成像应用中。在这样的分辨率下，大多数用于差异图像注册的软件包非常昂贵。结果，从业人员首先要大量删除原始图像，然后使用现有工具进行注册。我们的主要贡献是对降采样对注册性能的影响的广泛分析。我们通过将用Claire获得的全分辨率注册与合成和现实成像数据集的低分辨率注册进行比较，研究了这种影响。我们的结果表明，完全分辨率的注册可以产生卓越的注册质量 - 但并非总是如此。例如，将合成图像从$ 1024^3 $减少到$ 256^3 $将骰子系数从92％降低到79％。但是，对于嘈杂或低对比度的高分辨率图像，差异不太明显。克莱尔不仅允许我们在几秒钟内注册临床相关大小的图像，而且还可以在合理的时间内以前所未有的分辨率注册图像。考虑的最高分辨率是$ 2816 \ times3016 \ times1162 $的清晰图像。据我们所知，这是有关此类决议中图像注册质量的首次研究。

持续深度学习的领域是一个新兴领域，已经取得了很多进步。但是，同时仅根据图像分类的任务进行了大多数方法，这在智能车辆领域无关。直到最近才提出了班级开展语义分割的方法。但是，所有这些方法都是基于某种形式的知识蒸馏。目前，尚未对基于重播的方法进行调查，这些方法通常在连续的环境中用于对象识别。同时，尽管无监督的语义分割的域适应性获得了很多吸引力，但在持续环境中有关域内收入学习的调查并未得到充分研究。因此，我们工作的目的是评估和调整已建立的解决方案，以连续对象识别语义分割任务，并为连续语义分割的任务提供基线方法和评估协议。首先，我们介绍了类和域内的分割的评估协议，并分析了选定的方法。我们表明，语义分割变化的任务的性质在减轻与图像分类相比最有效的方法中最有效。特别是，在课堂学习中，学习知识蒸馏被证明是至关重要的工具，而在域内，学习重播方法是最有效的方法。

在文本情感分类中，相关标签的集合取决于域和应用程序方案，并且在模型开发时可能不知道。这与需要预定义的标签的经典学习范式相抵触。获得具有灵活标签的模型的解决方案是，将零局学习的范式用作自然语言推理任务，此外，它还增加了不需要任何标记的培训数据的优势。这就提出了一个问题，如何促使自然语言推断模型进行零击学习情绪分类。及时表述的选项包括单独的情感名称愤怒或“此文本表示愤怒”的陈述。在本文中，我们分析了基于自然推理的零射击分类器的敏感程度是对正在考虑的迅速考虑的更改：选择提示需要如何仔细选择？我们使用三种自然语言推论模型根据不同来源（推文，事件，博客）呈现不同语言寄存器的一组既定的情感数据集进行实验，并表明确实选择了特定及时配方的选择需要适合语料库。我们表明，可以通过多个提示的组合来应对这一挑战。与单个提示相比，这种合奏在整个语料库中更强大，并且与个人最佳提示的表现几乎相同。

我们提出了一种使用合理的心形和现实外观合成心脏MR图像的方法，目的是生成标记的数据进行深度学习（DL）训练。它将图像合成分解为标签变形和标签到图像翻译任务。前者是通过VAE模型中的潜在空间插值来实现的，而后者是通过条件GAN模型完成的。我们设计了一种在受过训练的VAE模型的潜在空间中的标记操纵方法，即病理合成，旨在合成一系列具有所需心脏病特征的伪病理合成受试者。此外，我们建议通过估计潜在矢量之间的相关系数矩阵来对2D切片之间的关系进行建模，并利用它在解码到图像空间之前将样品随机绘制的元素关联。这种简单而有效的方法导致从2D片段产生3D一致的受试者。这种方法可以提供一种解决方案，以多样化和丰富心脏MR图像的可用数据库，并为开发基于DL的图像分析算法的开发铺平道路。该代码将在https://github.com/sinaamirrajab/cardiacpathologysynthesis中找到。